Duplicate from RL-MIND/XHRBench

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+*.pcm filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+pretty_name: XHRBench
+license: apache-2.0
+task_categories:
+- image-text-to-text
+language:
+- en
+tags:
+- remote-sensing
+- mllm
+- vqa
+- image-captioning
+- high-resolution
+- multimodal
+- ultra-high-resolution
+- uav
+- benchmark
+---
+
+<table align="center" role="presentation" style="margin:0 auto; border:0; background:transparent;">
+  <tr style="border:0; background:transparent;">
+    <td align="center" style="border:0; background:transparent; padding:0 8px;">
+      <img src="./assets/rl-mind-logo-v2.webp" alt="RL-MIND research group logo" width="160" height="160" loading="eager" fetchpriority="high" decoding="async" style="width:160px; height:160px; object-fit:contain;" />
+    </td>
+    <td align="center" style="border:0; background:transparent; padding:0 8px;">
+      <img src="./assets/dataset-logo-v2.webp" alt="XHRBench dataset logo" width="160" height="160" loading="eager" fetchpriority="high" decoding="async" style="width:160px; height:160px; object-fit:contain;" />
+    </td>
+  </tr>
+</table>
+
+<h1 align="center">XHRBench</h1>
+<p align="center"><b>Ultra-High-Resolution Remote Sensing Understanding and Reasoning</b></p>
+
+<p align="center">
+  <a href="https://huggingface.co/datasets/RL-MIND/XHRBench">🤗 Hugging Face</a> ·
+  <a href="https://modelscope.cn/datasets/KAIWANG/XHRBench">🤖 ModelScope</a> ·
+  <a href="https://arxiv.org/abs/2512.17319">📄 Paper</a> ·
+  <a href="https://github.com/Yunkaidang/RSHR">💻 Code</a>
+</p>
+<p align="center"><a href="https://huggingface.co/datasets/RL-MIND/XHRBench">English</a> | <a href="https://huggingface.co/datasets/RL-MIND/XHRBench/blob/main/README_ZH.md">中文</a></p>
+
+---
+## 📚 Introduction
+
+**XHRBench** evaluates fine-grained perception and complex reasoning in multimodal large language models using ultra-high-resolution remote-sensing imagery. This repository retains the name **XHRBench** and belongs to the same RSHR benchmark project as [RSHR-Bench](https://huggingface.co/datasets/RL-MIND/RSHR-Bench), with a different file layout.
+
+The full benchmark described in the paper contains **5,329 full-scene images**, with a long side of at least **4,000 pixels** and up to approximately **3 × 10⁸ pixels (300 MP)** per image. This repository's `dataset.json` contains **1,932 VQA examples**; the package's example count is distinct from the full benchmark's image count.
+
+## 🎯 Dataset at a Glance
+
+| Item | Details |
+| --- | --- |
+| Current JSON release | 1,932 VQA examples |
+| Task categories | 17 original category identifiers |
+| Images per example | 1 image: 1,862; 2 images: 50; 3 images: 20 |
+| Data language | English |
+| Main files | `dataset.json`, `images/`, `data/train-00000-of-00001.parquet` |
+| Full benchmark scope | 5,329 full-scene images; 9 perception categories and 4 reasoning types |
+
+The benchmark draws on **DOTA-v2.0, MiniFrance, FAIR1M, HRSCD, XLRS-Bench**, and the team's **100 MP UAV imagery**. Its two-stage **Human–LLM Adversarial Verification** pipeline combines LLM adversarial filtering with human review to reduce questions answerable from language priors alone.
+
+## 🌟 Task Design
+
+| Task family | Evaluation focus |
+| --- | --- |
+| Multiple-Choice VQA (MCQ) | Visual decisions within a fixed answer space, including single-turn and multi-turn dialogues |
+| Open-Ended VQA (OEQ) | Free-form visual understanding and compositional reasoning |
+| Image Captioning (IC) | Global scene summaries and regional descriptions |
+| Single-Image Evaluation (SIE) | Multi-scale perception and reasoning within an ultra-high-resolution image |
+
+These four families describe the full evaluation design in the paper. The `dataset.json` in this repository contains VQA annotations with answer options. See the [paper](https://arxiv.org/abs/2512.17319) and [code](https://github.com/Yunkaidang/RSHR) for the complete evaluation protocol.
+
+## 📦 Data Format
+
+```text
+dataset.json                       # List of VQA annotations
+images/                            # Original images referenced by annotations
+data/train-00000-of-00001.parquet  # Repository-provided Parquet file
+assets/                            # Dataset-card figures and logos
+```
+
+| Field | Description |
+| --- | --- |
+| `id` | Sample identifier |
+| `images` | List of relative image paths |
+| `question` | Question text |
+| `options` | Mapping from option letters to option text |
+| `answer` | Gold answer option |
+| `category` | Original task category identifier |
+
+Image paths are relative to the repository root. Category identifiers retain their original spelling. The `train` component in the Parquet filename is a packaging label; it does not by itself define the paper's training/test protocol.
+
+### Category distribution
+
+| Category (original identifier) | Examples |
+| --- | ---: |
+| `anomaly_detection` | 50 |
+| `color` | 200 |
+| `detection` | 100 |
+| `future_prediction_two_image` | 50 |
+| `multi_region_join_contrast` | 20 |
+| `multi_region_join_contrast_singal` | 50 |
+| `multitrun-future_prediction` | 150 |
+| `multiturn_object_state_judgement` | 132 |
+| `multturn_anomaly_detection` | 60 |
+| `object_clasfication` | 200 |
+| `object_counting` | 200 |
+| `object_grounding` | 200 |
+| `object_state_judgement` | 50 |
+| `regional_grouding` | 70 |
+| `regionla_counting` | 100 |
+| `relation` | 200 |
+| `shape` | 100 |
+| **Total** | **1,932** |
+
+## 🚀 Quick Start
+
+Inspect the annotations first, then download the images you need.
+
+```python
+import json
+from huggingface_hub import hf_hub_download
+
+annotation_path = hf_hub_download(
+    repo_id="RL-MIND/XHRBench",
+    repo_type="dataset",
+    filename="dataset.json",
+)
+with open(annotation_path, encoding="utf-8") as f:
+    samples = json.load(f)
+
+sample = samples[0]
+print(sample["category"], sample["question"], sample["options"])
+
+# Download only the image needed for this sample.
+image_path = hf_hub_download(
+    repo_id="RL-MIND/XHRBench",
+    repo_type="dataset",
+    filename=sample["images"][0],
+)
+```
+
+## 📊 Evaluation and Visualizations
+
+The original project reports evaluations of **14 models**, including GPT-4o, Gemini 1.5 Pro, Qwen2.5-VL, and remote-sensing models such as GeoChat and VHM, across multiple-choice VQA, open-ended VQA, and image captioning. The original overview, leaderboard, and task-distribution figures are retained below; consult the paper for model versions and evaluation settings.
+
+![Benchmark overview](./assets/image.png)
+
+### Evaluation figures
+
+![Multiple-choice VQA leaderboard](./assets/image_203.png)
+
+<p align="center">
+  <img src="./assets/image_201.png" alt="Task distribution" width="580" />
+</p>
+
+## 💡 Usage Notes
+
+- Intended for evaluating visual understanding, perception, and reasoning in ultra-high-resolution remote sensing.
+- Original high-resolution images can require substantial disk space and decoding memory; download them selectively when possible.
+- Use `dataset.json` together with `images/` and preserve the relative paths.
+- This repository and RSHR-Bench use different packages. File and shard counts should not be used as direct measures of benchmark size.
+
+## 📜 License and Citation
+
+The repository declares the **Apache-2.0** license. The imagery draws on other public remote-sensing datasets; consult the original licenses and terms when using those source datasets.
+
+This dataset is associated with [A Benchmark for Ultra-High-Resolution Remote Sensing MLLMs](https://arxiv.org/abs/2512.17319). Please cite the paper when using the benchmark:
+
+```bibtex
+@article{dang2025RSHR,
+  title={A Benchmark for Ultra-High-Resolution Remote Sensing MLLMs},
+  author={Dang, Yunkai and Zhu, Meiyi and Wang, Donghao and Zhang, Yizhuo and Yang, Jiacheng and Fan, Qi and Yang, Yuekun and Li, Wenbin and Miao, Feng and Gao, Yang},
+  journal={arXiv preprint arXiv:2512.17319},
+  year={2025}
+}
+```

README_ZH.md

@@ -0,0 +1,158 @@
+<table align="center" role="presentation" style="margin:0 auto; border:0; background:transparent;">
+  <tr style="border:0; background:transparent;">
+    <td align="center" style="border:0; background:transparent; padding:0 8px;">
+      <img src="./assets/rl-mind-logo-v2.webp" alt="RL-MIND research group logo" width="160" height="160" loading="eager" fetchpriority="high" decoding="async" style="width:160px; height:160px; object-fit:contain;" />
+    </td>
+    <td align="center" style="border:0; background:transparent; padding:0 8px;">
+      <img src="./assets/dataset-logo-v2.webp" alt="XHRBench dataset logo" width="160" height="160" loading="eager" fetchpriority="high" decoding="async" style="width:160px; height:160px; object-fit:contain;" />
+    </td>
+  </tr>
+</table>
+
+<h1 align="center">XHRBench</h1>
+<p align="center"><b>超高分辨率遥感视觉理解与推理基准</b></p>
+
+<p align="center">
+  <a href="https://huggingface.co/datasets/RL-MIND/XHRBench">🤗 Hugging Face</a> ·
+  <a href="https://modelscope.cn/datasets/KAIWANG/XHRBench">🤖 ModelScope</a> ·
+  <a href="https://arxiv.org/abs/2512.17319">📄 论文</a> ·
+  <a href="https://github.com/Yunkaidang/RSHR">💻 代码</a>
+</p>
+<p align="center"><a href="https://huggingface.co/datasets/RL-MIND/XHRBench">English</a> | <a href="https://huggingface.co/datasets/RL-MIND/XHRBench/blob/main/README_ZH.md">中文</a></p>
+
+---
+## 📚 简介
+
+**XHRBench** 面向多模态大语言模型的超高分辨率遥感细粒度感知与复杂推理评测。本仓库保留 **XHRBench** 名称，与 [RSHR-Bench](https://huggingface.co/datasets/RL-MIND/RSHR-Bench) 同属 RSHR 基准项目；两个仓库的文件组织方式不同。
+
+论文中的完整基准包含 **5,329 张全景图像**，长边至少 **4,000 像素**，单张图像最高约 **3 × 10⁸ 像素（300 MP）**。本仓库 `dataset.json` 提供 **1,932 条 VQA 样本**；这一发布文件的样本数不等于完整基准的图像总数。
+
+## 🎯 数据概览
+
+| 项目 | 内容 |
+| --- | --- |
+| 当前 JSON 发布 | 1,932 条 VQA 样本 |
+| 任务类别 | 17 个原始类别标识 |
+| 图像数量分布 | 单图 1,862 条；双图 50 条；三图 20 条 |
+| 数据语言 | 英语 |
+| 主要文件 | `dataset.json`、`images/`、`data/train-00000-of-00001.parquet` |
+| 完整基准范围 | 5,329 张全景图像；9 类感知能力、4 类推理能力 |
+
+基准数据来源包括 **DOTA-v2.0、MiniFrance、FAIR1M、HRSCD、XLRS-Bench** 以及团队采集的 **100 MP 无人机影像**。问题采用“**LLM 对抗筛选 + 人工复核**”的两阶段验证流程，减少仅依靠语言先验即可回答的问题。
+
+## 🌟 任务设计
+
+| 任务族 | 评测内容 |
+| --- | --- |
+| 多项选择视觉问答（MCQ） | 固定选项空间中的视觉决策，覆盖单轮与多轮对话 |
+| 开放式视觉问答（OEQ） | 自由形式的视觉理解与组合推理 |
+| 图像描述（IC） | 全局场景概括与区域细节描述 |
+| 单图评测（SIE） | 超高分辨率单图上的多尺度感知与推理 |
+
+上述四个任务族描述论文中的完整评测设计；当前仓库的 `dataset.json` 为带选项的 VQA 标注。完整评测协议请参阅[论文](https://arxiv.org/abs/2512.17319)与[代码](https://github.com/Yunkaidang/RSHR)。
+
+## 📦 数据格式
+
+```text
+dataset.json                       # VQA 标注列表
+images/                            # 标注引用的原始图像
+data/train-00000-of-00001.parquet  # 仓库提供的 Parquet 文件
+assets/                            # 数据集卡片图示与标识
+```
+
+| 字段 | 含义 |
+| --- | --- |
+| `id` | 样本标识 |
+| `images` | 图像的相对路径列表 |
+| `question` | 问题文本 |
+| `options` | 从选项字母到选项文本的映射 |
+| `answer` | 标准答案选项 |
+| `category` | 原始任务类别标识 |
+
+图像路径相对于仓库根目录。类别名称保留原始拼写，使用时请按原值读取。Parquet 文件名中的 `train` 是发布文件命名；它本身不定义论文的训练/测试划分。
+
+### 类别分布
+
+| 任务类别（原始标识） | 样本数 |
+| --- | ---: |
+| `anomaly_detection` | 50 |
+| `color` | 200 |
+| `detection` | 100 |
+| `future_prediction_two_image` | 50 |
+| `multi_region_join_contrast` | 20 |
+| `multi_region_join_contrast_singal` | 50 |
+| `multitrun-future_prediction` | 150 |
+| `multiturn_object_state_judgement` | 132 |
+| `multturn_anomaly_detection` | 60 |
+| `object_clasfication` | 200 |
+| `object_counting` | 200 |
+| `object_grounding` | 200 |
+| `object_state_judgement` | 50 |
+| `regional_grouding` | 70 |
+| `regionla_counting` | 100 |
+| `relation` | 200 |
+| `shape` | 100 |
+| **总计** | **1,932** |
+
+## 🚀 快速开始
+
+先读取标注，再按需下载图像。
+
+```python
+import json
+from huggingface_hub import hf_hub_download
+
+annotation_path = hf_hub_download(
+    repo_id="RL-MIND/XHRBench",
+    repo_type="dataset",
+    filename="dataset.json",
+)
+with open(annotation_path, encoding="utf-8") as f:
+    samples = json.load(f)
+
+sample = samples[0]
+print(sample["category"], sample["question"], sample["options"])
+
+# Download only the image needed for this sample.
+image_path = hf_hub_download(
+    repo_id="RL-MIND/XHRBench",
+    repo_type="dataset",
+    filename=sample["images"][0],
+)
+```
+
+## 📊 评测与可视化
+
+原始项目报告评测了 **14 个模型**，包括 GPT-4o、Gemini 1.5 Pro、Qwen2.5-VL，以及 GeoChat、VHM 等遥感模型，覆盖多项选择问答、开放式问答与图像描述。以下保留原始发布的概览、排行榜和任务分布图；具体模型版本与评测设置请参阅论文。
+
+![Benchmark overview](./assets/image.png)
+
+### 评测图示
+
+![Multiple-choice VQA leaderboard](./assets/image_203.png)
+
+<p align="center">
+  <img src="./assets/image_201.png" alt="Task distribution" width="580" />
+</p>
+
+## 💡 使用说明
+
+- 用于评估超高分辨率遥感视觉理解、感知与推理能力。
+- 原始超高分辨率图像可能需要较多磁盘空间和解码内存，建议按需下载。
+- `dataset.json` 与 `images/` 配套使用；保持相对路径完整。
+- 本仓库与 RSHR-Bench 的打包形式不同，不应直接将文件数或分片数作为基准规模比较。
+
+## 📜 许可与引用
+
+仓库声明的许可证为 **Apache-2.0**。遥感图像来源包括其他公开数据集；使用相关源数据时，请同时查看其原始许可与使用条款。
+
+相关论文为 [A Benchmark for Ultra-High-Resolution Remote Sensing MLLMs](https://arxiv.org/abs/2512.17319)。如果本数据集对研究有所帮助，请引用：
+
+```bibtex
+@article{dang2025RSHR,
+  title={A Benchmark for Ultra-High-Resolution Remote Sensing MLLMs},
+  author={Dang, Yunkai and Zhu, Meiyi and Wang, Donghao and Zhang, Yizhuo and Yang, Jiacheng and Fan, Qi and Yang, Yuekun and Li, Wenbin and Miao, Feng and Gao, Yang},
+  journal={arXiv preprint arXiv:2512.17319},
+  year={2025}
+}
+```

data/train-00000-of-00001.parquet

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